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Integrated soil amendments for potato crop performance

2023· article· en· W4390760581 on OpenAlexaff
Shivam Singh, Neeraj Kumar Singh, Anjana Kholia, Umesh Chandra Sati, Akshay Chittora, Vijai Kumar

Bibliographic record

VenueRASSA Journal of Science for Society · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPotato Plant Research
Canadian institutionsCentre de Santé et de Services Sociaux Cavendish
Fundersnot available
KeywordsCropAgronomyEnvironmental scienceAgricultural engineeringAgroforestryBiologyEngineering

Abstract

fetched live from OpenAlex

Present study on integrated soil amendments for potato crop performance was conducted at the Horticultural Research Farm of Chaudhary Shivnath Singh Shandilya (P.G.) College, Machhra, Meerut (U.P.) during rabi season of the year 2020-21. The experiment was laid out in RBD (Randomized Block Design) with nine treatments consisting of different organic and inorganic fertilizers, both alone and in combination, replicated thrice. Observations on growth parameters at 60 and 90 days after planting (DAP), yield parameters at the time of harvest and quality parameters after harvest were recorded for statistical analysis. The result obtained indicated superiority of combinations of inorganic and organic fertilizers as compared to the other treatments in terms of growth and yield parameters. The maximum plant height, greater number of leaves per hill and greater tuber diameter were observed with treatment receiving half of the recommended dose of fertilizers (RDF) and 20 t/ha FYM, while the maximum number of haulms per hill and higher dry matter content was observed in treatment receiving 5 t/ha vermicompost. More number of branches per plant, yield characters i.e., number of tubers/ plant, weight of tuber, average tuber weight, and yield of tuber were recorded in treatment receiving full RDF along with 5 t/ha vermicompost.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.518
Threshold uncertainty score0.572

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.054
GPT teacher head0.305
Teacher spread0.251 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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